A Theoretical Study of Anomaly Detection in Big Data Distributed Static and Stream Analytics

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

A high volume of data including log records, sensors, monitoring systems, manufacturing processes, call detail records, blogs, emails, and social media streams are generated around the clock by diverse applications. Thus, as the volume of data is growing rapidly, detecting anomaly from high volume big data becomes a critical and difficult task, due to the theatrical (research) and practical (technical) limitations. This paper aims to investigate anomaly detection and provide global understanding of anomaly concepts in the big data mining perspective. In this paper we demonstrate how existing methods of anomaly detection can be adopted with high volumes of data, specifically providing in depth understanding of the anomaly concept in streaming data. The key contribution of this study is an attempt to answer the following questions: 1) What is the concept of big data and what are big data analytic approaches? 2) What is the relationship between big data and anomaly detection? 3) What is the main characteristic of anomaly in big batch and streaming data? 4) What is the appropriate state of the art infrastructure to process and detect large-scale batch and streaming data?

Original languageEnglish
Title of host publicationProceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
EditorsJuan E. Guerrero
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1177-1182
Number of pages6
ISBN (Electronic)9781538666142
ISBN (Print)9781538666159
DOIs
Publication statusPublished - 24 Jan 2019
Event20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems - Exeter, United Kingdom
Duration: 28 Jun 201830 Jun 2018
Conference number: 20/16/4

Conference

Conference20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems
Abbreviated titleHPCC/SmartCity/DSS 2018
CountryUnited Kingdom
CityExeter
Period28/06/1830/06/18

Fingerprint

Dive into the research topics of 'A Theoretical Study of Anomaly Detection in Big Data Distributed Static and Stream Analytics'. Together they form a unique fingerprint.

Cite this